Lecture

Automated Chemical Synthesis: Catalyst Design & Optimization

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In course

ChE-605: AI in chemistry and beyond:Highlights in the field

Should have expertise in chemistry, physics or lite and material sciences. Although a very good knowledge in Al-based algorithms is required to fully understand the technical details, a basic knowledge is sufficient to understand the potential of these met ...

Description

This lecture explores the automation of chemical synthesis, focusing on catalyst discovery and optimization using machine learning and computational chemistry. The Gomes Group at the University is at the forefront of developing end-to-end frameworks for catalyst design. Through machine-guided reaction optimization and autonomous catalyst discovery platforms, the team aims to revolutionize the field. The lecture delves into the development of virtual libraries, generative models, and regression techniques for predicting and optimizing catalyst properties. By leveraging advanced computational tools and innovative approaches, the Gomes Group is paving the way for autonomous catalyst discovery and optimization.

Instructor

Philippe Schwaller

Official source

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Computational chemistry

Computational chemistry is a branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into computer programs, to calculate the structures and properties of molecules, groups of molecules, and solids. It is essential because, apart from relatively recent results concerning the hydrogen molecular ion (dihydrogen cation, see references therein for more details), the quantum many-body problem cannot be solved analytically, much less in closed form.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

Total synthesis

Total synthesis is the complete chemical synthesis of a complex molecule, often a natural product, from simple, commercially-available precursors. It usually refers to a process not involving the aid of biological processes, which distinguishes it from semisynthesis. Syntheses may sometimes conclude at a precursor with further known synthetic pathways to a target molecule, in which case it is known as a formal synthesis. Total synthesis target molecules can be natural products, medicinally-important active ingredients, known intermediates, or molecules of theoretical interest.

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